Traffic-Light Detection and Classification Using Computer Vision and Deep Learning

ABSTRACT

A method is disclosed for detecting and classifying one or more traffic lights. The method may include converting an RGB frame to an HSV frame. The HSV frame may be filtered by at least one threshold value to obtain at least one saturation frame. At least one contour may be extracted from the at least one saturation frame. Accordingly, a first portion of the RGB may be cropped in order to encompass an area including the at least one contour. The first portion may then be classified by an artificial neural network to determined whether the first portion corresponds to a not-a-traffic-light class, a red-traffic-light class, a green-traffic-light class, a yellow-traffic-light class, or the like.

CROSS REFERENCE TO RELATED PATENT APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 15/360,883, filed on Nov. 23, 2016, which is incorporated byreference in its entirety.

BACKGROUND Field of the Invention

This invention relates to vehicular systems and more particularly tosystems and methods for using image-based detection and classificationof traffic lights as a basis for controlling one or more functions of avehicle.

Background of the Invention

Traffic lights communicate vital information. Any failure to properlydetect and classify a traffic light is a serious safety violation thatmay result in death, injury, and/or significant damage to property.Accordingly, what are needed are computerized systems that reliablydetect and classify traffic lights.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered limiting of its scope, the invention will be describedand explained with additional specificity and detail through use of theaccompanying drawings, in which:

FIG. 1 is a schematic diagram illustrating an exemplary image that maybe captured by a forward-looking camera carried on-board a vehicle;

FIG. 2 is a schematic diagram illustrating one embodiment of a vehiclecarrying on-board a system for detecting and classifying traffic lightsin accordance with the present invention;

FIG. 3 is a schematic block diagram illustrating one embodiment of asystem for detecting and classifying traffic lights in accordance withthe present invention;

FIG. 4 is a schematic block diagram illustrating one embodiment of acomputer-vision module in accordance with the present invention;

FIG. 5 is a schematic diagram illustrating the flow from the exemplaryimage of FIG. 1, to an intermediate image, and on to a channel-specific,threshold image;

FIG. 6 is a schematic diagram illustrating certain candidate-filteringaspects of computer vision implemented by a computer-vision module inaccordance with the present invention; and

FIG. 7 is a schematic block diagram of one embodiment of a method fordetecting and classifying traffic lights in accordance with the presentinvention.

DETAILED DESCRIPTION

It will be readily understood that the components of the presentinvention, as generally described and illustrated in the Figures herein,could be arranged and designed in a wide variety of differentconfigurations. Thus, the following more detailed description of theembodiments of the invention, as represented in the Figures, is notintended to limit the scope of the invention, as claimed, but is merelyrepresentative of certain examples of presently contemplated embodimentsin accordance with the invention. The presently described embodimentswill be best understood by reference to the drawings, wherein like partsare designated by like numerals throughout.

Referring to FIGS. 1 and 2, traffic lights 10 communicate vitalinformation that must be properly assimilated for a vehicle 12 to besafely operated. This reality creates significant challenges for driversand for autonomous driving systems. For example, if a driver isdistracted, he or she may not properly detect and/or respond to atraffic light 10. Similarly, since traffic lights 10 are ubiquitous,autonomous driving systems may be required to detect and respond to avariety of traffic lights 10.

To overcome these challenges, a vehicle 12 in accordance with thepresent invention may carry on-board a system 14 tasked with detectingand classifying traffic lights. A system 14 in accordance with thepresent invention may use the outputs of one or more on-board sensors 16as the basis for such detection and classification. The sensors 16included within a vehicle 12 may take any suitable form. For example,one or more sensors 16 may comprise cameras, light sensors,speedometers, braking sensors, steering sensors, throttle sensors, orthe like or a combination or sub-combination thereof.

In selected embodiments, a system 14 in accordance with the presentinvention may control one or more functions of a vehicle 12. Forexample, a system 14 may control the operation of one or more warningsystems of a vehicle 12. Accordingly, should the data from one or moresensors 16 indicate that a vehicle 12 is approaching a red light withexcessive speed (e.g., no braking, too little braking, or the like), asystem 14 may instruct one or more warning systems to issue one or morewarnings (e.g., flash a light, sound an alarm, vibrate a steering wheel,or the like). Alternatively, or in addition thereto, a system 14 maycontrol the operation of one or more core systems of a vehicle 12.Accordingly, should the data from one or more sensors 16 indicate that avehicle 12 is approaching a red light with excessive speed, a system 14may activate the brakes of the vehicle 12.

In certain embodiments, one or more sensors 16 in accordance with thepresent invention may be forward-facing or forward-looking cameras 16 a(e.g., cameras directed to an area ahead of a vehicle 12), point-of-viewcameras 16 b (e.g., cameras capturing a driver's point of view through awindshield), or the like or a combination or sub-combination thereof.

Image data (e.g., video) captured by one or more sensors 16 may beprocessed by a system 14 as individual images 18 or frames 18. Forexample, an artificial neural network within a system 14 may be fedselected portions of one or more images 18 captured by one or moresensors 16. The artificial neural network may take such portions intoconsideration as it determines which class (e.g., which traffic light10) is represented therein. Accordingly, a system 14 may control one ormore functions of a vehicle 12 in accordance with classifications madeby an artificial neural network.

In certain embodiments, the classifications performed by an artificialneural network may occur in real time with the capturing of the sensordata upon which the classification is based. That is, an artificialneural network may quantify the correspondence of particular sensor data(e.g., image data) to one or more classes within a very short period oftime after the capture of that particular sensor data by one or moresensors 16. In selected embodiments, that very short period of time maybe about one second or less.

Referring to FIG. 3, a system 14 in accordance with the presentinvention may generate or collect information characterizing one or moretraffic lights 10 using computer vision, deep learning, or a combinationthereof. A system 14 may accomplish this in any suitable manner. Forexample, a system 14 may be embodied as hardware, software, or somecombination thereof.

In selected embodiments, a system 14 may include computer hardware andcomputer software. The computer hardware of a system 14 may include oneor more processors 20, memory 22, one or more user interfaces 24, otherhardware 26, or the like or a combination or sub-combination thereof. Incertain embodiments, all or some subset of this computer hardware may behardware already included as part of a vehicle 12. That is, all or someportion of the computer hardware may be multipurpose and perform tasksthat are already associated with the operation of the vehicle 12.Alternatively, a system 14 in accordance with the present invention maybe dedicated exclusively to detecting, classifying, and/or responding totraffic lights 10.

The memory 22 of a system 14 in accordance with the present inventionmay be operably connected to the one or more processors 20 and store thecomputer software. This may enable the one or more processors 20 toexecute the computer software. Thus, a system 14 may augment thefunctionality or features of a vehicle 12 by adding and/or modifyingsoftware, adding additional hardware to the vehicle 12, or a combinationthereof.

A user interface 24 of a system 14 may enable an engineer, technician,driver, or the like to interact with, run, customize, or control variousaspects of a system 14. A user interface 24 of a system 14 may includeone or more buttons, keypads, keyboards, touch screens, pointingdevices, or the like or a combination or sub-combination thereof.Alternatively, or in addition thereto, a user interface 24 may compriseone or more communication ports (e.g., plug in ports, wirelesscommunication ports, etc.) through which one or more external computersor devices may communicate with a system 14 or one or more componentsthereof. In certain embodiments, a user interface 24 may enable a user(e.g., a driver) to manually control (e.g., select, incrementallyincrease or decrease) one or more warnings to be issued in one or moretraffic-light-related situations.

In selected embodiments, the memory 22 of a system 14 may store (atleast temporality) sensor data 28 (e.g., one or more segments of signaloutput by one or more sensors 16 carried on-board a vehicle 12), video30 (e.g., one or more video files 30) collected or captured by one ormore sensors 16 carried on-board a vehicle 12, one or more images files32 containing, defining, or corresponding to one or more images capturedby one or more sensors 16 or extracted from video collected or capturedby one or more sensors 16, or the like or a combination orsub-combination thereof.

Additionally, the memory 22 may store one or more software modules. Forexample, the memory 22 may store a communication module 34,image-extraction module 36, computer-vision module 38, neural networkmodule 40, control module 42, other data or software 44, or the like ora combination or sub-combinations thereof. Alternatively, one or more ofthe communication module 34, image-extraction module 36, computer-visionmodule 38, neural network module 40, control module 42 may be embodiedas hardware or comprise hardware components. Thus, while FIG. 3 showsthe communication module 34, image-extraction module 36, computer-visionmodule 38, neural network module 40, and control module 42 as beingsoftware-only modules that are stored in memory 22, in actuality, one ormore of these modules 34, 36, 38, 40, 42 may comprise hardware,software, or a combination thereof.

A communication module 34 may enable data such as one or more segmentsof sensor data 28, video files 30, image files 32, software components(e.g., one or more modules 34, 36, 38, 40, 42 or updates thereto),information characterizing a traffic light 10, classifications (e.g.,classification information output by the artificial neural network of asystem 14), or the like or combinations of sub-combinations thereof tobe passed into or out of a system 14 in accordance with the presentinvention. For example, a communication module 34 forming part of asystem 14 carried on-board a vehicle 12 may enable the system 14 toreceive an update to its computer-vision module 38, neural networkmodule 40, or the like. Accordingly, improvements developed off-board avehicle 12 may be brought on-board as desired or necessary.

An image-extraction module 36 may extract one or more images 18 fromvideo captured by one or more sensors 16. For example, animage-extraction module 34 may extract one or more images 18 from avideo file 30 that is stored in memory 22, video that is being output bya sensor 16, or the like. In selected embodiments, an image-extractionmodule 36 may store one or more images 18 that are extracted thereby asimages files 32 in memory 22.

A computer-vision module 38 may determine which portions of one or moreimages 18 should be processed by a neural network module 40. Forexample, a computer-vision module 38 may identify and crop one or moreportions of an image 18 that are more likely to contain a traffic light10. Such portions may then be passes to a neural network module 40 to beclassified.

A neural network module 40 may be, include, or support an artificialneural network programmed to perform or apply deep learning. The deeplearning performed or applied by an artificial neural network may useone or more algorithms to model high-level abstractions in datacorresponding to one or more portions of one or more images 18 collectedby the one or more sensors 16 connected to a system 14 in accordancewith the present invention. In selected embodiments, this may beaccomplished by using multiple processing layers comprising multiplenon-linear transformations.

For example, an artificial neural network corresponding to a neuralnetwork module 40 may comprise feed-forward computational graphs withinput nodes, hidden layers and output nodes. For classifications thatinvolve images, pixel-values of an input image forming part of theclassification may be assigned to input nodes, and then be fed throughthe network, passing a number of non-linear transformations. At the endof the computation, the output node may yield a value that correspondsto the class inferred by the neural network.

In order for an artificial neural network corresponding to a neuralnetwork module 40 to be able to distinguish between different classes,it needs to be trained based on examples. Accordingly, to create anartificial neural network that is able to classify a plurality ofdifferent traffic lights, a large collection of example images (e.g.,hundreds to thousands for each type) having known (e.g., labeled)characteristics must be used as training data. Thus, usingbackpropagation, an artificial neural network may be trained.

An artificial neural network corresponding to a neural network module 40may be trained while operating within or on the hardware anon-production system 14. For example, an artificial neural network maybe trained on an off-board system 14 in a computer laboratory, anon-production system 14 carried on-board a test vehicle 12 specificallyfor the purposes of training, or the like. Once trained, an artificialneural network may be “cloned” or otherwise copied onto or importedwithin a production system 14 forming part of a production vehicle 12.

When trained, an artificial neural network corresponding to a neuralnetwork module 40 may receive one or more inputs (e.g., cropped portionsof one or more images 18 captured by one or more sensors 16) andclassify those inputs as having a particular numeric affinity (e.g.,percentage “score”) for each class for which the artificial neuralnetwork was trained. Accordingly, if an artificial neural network weretrained on ten different classes, then for one or more inputs, theartificial neural network may output ten numeric scores. Each such scoremay be indicative of the affinity of the one or more inputs (or of thephysical reality reflected by the one or more inputs) to a differentclass.

In a decisive or clear classification, the one or more inputs may show astrong affinity to one class and weak affinity to all other classes. Inan indecisive or unclear classification, the one or more inputs may showno preferential affinity to any particular class. For example, there maybe a “top” score for a particular class, but that score may be close toother scores for other classes.

Accordingly, in selected embodiments, a neural network module 40 mayapply one or more threshold comparisons or tests to determine whetherany particular classification is sufficiently decisive or clear so as tobe acted or relied upon (e.g., whether the classification issufficiently decisive or clear so as to merit some change to thefunctioning of a vehicle 12). For example, a neural network module 40may test a classification to see if the separation between a top scoreand all other scores meets or satisfies a certain separation threshold.

An artificial neural network in accordance with the present inventionmay be trained to recognize (e.g., produce affinity scores for) acertain, predetermined set of classes. The number of classes within sucha set may vary between embodiments. In certain embodiments, the numberof classes may be one greater than the types of traffic lights acorresponding vehicle 12 is lightly to encounter. For example, if avehicle 12 were only likely to encounter three types of traffic lights(e.g., a circular red light, a circular yellow light, and a circulargreen light), the number of classes may be four and include anot-a-traffic-light class, red-traffic-light class, yellow-traffic-lightclass, and green-traffic-light class.

However, in most driving environments, a vehicle 12 will encounter morethan three types of traffic lights 10. Accordingly, an artificial neuralnetwork may more typically be trained to recognized more than fourclasses. For example, in selected embodiments, an artificial neuralnetwork in accordance with the present invention may be trained torecognize a not-a-traffic-light class, red-traffic-light class,yellow-traffic-light class, green-traffic-light class, red-left-arrowclass, red-right-arrow class, yellow-left-arrow class,yellow-right-arrow class, green-left-arrow class, green-right-arrowclass, or the like or a combination or sub-combination thereof.

A control module 42 may be programmed to request, initiate, or implementone or more actions or functions based on selected sensor data 28, theclassifications determined by a neural network module 40, or the like ora combination thereof. For example, when a control module 40 determinesfrom the data 28 and classifications that a vehicle 12 is approaching ared light with excessive speed, the control module 42 may request,initiate, or implement one or more warnings (e.g., a flashing light, asounding alarm, vibration of a steering wheel, or the like or acombination or sub-combination thereof). Alternatively, or in additionthereto, the control module 42 may request, initiate, or implementbraking of the vehicle 12.

Referring to FIGS. 4-6, a computer-vision module 38 may determine in anysuitable manner which portions of one or more images 18 are to beprocessed by a neural network module 40. In selected embodiments, toperform its intended function, a computer-vision module 38 may includean intensity module 46, conversion module 48, channel-filter module 50,contour-detection module 52, candidate-filter module 54, crop module 56,export module 58, other data or software 60, or the like or acombination or sub-combination thereof.

An intensity module 46 may determine whether an image 18 corresponds toa day condition or a night condition. In selected embodiments, anintensity module 46 may determine an average intensity of the pixels inan upper portion 62 of the image 18 at issue. If the average intensityis greater than or equal to a particular threshold, the intensity module46 may determine that the image 18 corresponds to or represents (e.g.,captures) a day condition. If the average intensity is less than theparticular threshold, the intensity module 46 may determine that theimage 18 corresponds to or represents (e.g., captures) a nightcondition.

The upper portion 62 analyzed by an intensity module 46 may be definedin any suitable manner. As illustrated, the upper portion 62 maycomprises all pixels within a particular window or box. However, inselected embodiments, the upper portion 62 may simply be the top half,third, quarter, or the like of the image 18.

Most commonly, the color of the various pixels of an image 18corresponding to one or more sensors 16 (e.g., one or more cameras) maybe defined in terms of RGB values. For example, the color of each pixelmay have a red value from 0 to 255, a green value from 0 to 255, and ablue value from 0 to 255. However, such a color scheme may not readilyreveal all the information used by systems 14 in accordance with thepresent invention. Accordingly, in selected embodiments, a conversionmodule 48 may convert an image 18 into an intermediate image 64 that hasa color scheme from which more useful information may be extracted.

In selected embodiments, a conversion module 48 may treat “day” and“night” images 18 differently. That is, when an intensity module 46determines that an image 18 corresponds to a day condition, a conversion48 may convert that image 18 to an intermediate image 64 in a firstcolor scheme or space. When an intensity module 46 determines that animage 18 corresponds to a night condition, a conversion module 48 mayconvert that image 18 to an intermediate image 64 in a second, differentcolor scheme or space.

For example, a conversion module 48 may convert “day” RGB images 18 HSV,HSB, HSL, or HSI intermediate images 64 and convert “night” RGB images18 to LAB intermediate images 64. Intermediate images 64 correspondingto HSV, HSB, HSL, or HIS may have color schemes or color spaces withpixel colors represented by two scores or values corresponding to “hue”and “saturation” and a third score or value corresponding to “value,”“brightness,” “lightness” or “luminosity”, and “intensity,”respectively. Intermediate images 64 corresponding to LAB may have acolor scheme or color space with pixel colors represented by scores orvalues corresponding to “lightness” and “a” and “b” color-opponentdimensions.

A channel-filter module 50 may produce one or more channel-specific,threshold images 66 from one or more intermediate images 64. A channelmay correspond to or be a particular component of a color scheme orcolor space. For example, in the HSV, HSB, HSL, or HSI color scheme orcolor space, the “saturation” component of color may correspond to or bea channel. Similarly, in the LAB color scheme or color space, the “a”component of color may correspond to or be a channel. Accordingly, achannel-filter module 50 may process one or more intermediate images 64to produce one or more channel-specific, threshold images 66 byfiltering out all pixels having below a particular threshold on“saturation” and/or “a” channels.

For a particular intermediate image 64, a channel-filter module 50 mayproduce more than one channel-specific, threshold image 66. In suchembodiments, different channel-specific, threshold images 66 maycorrespond to different thresholds. For example, a channel-filter module50 may filter the “saturation” channel of an intermediate image 64corresponding to a “day” condition at multiple threshold values (e.g.,50, 100, 150, 200, 250, or the like or a combination or sub-combinationthereof) within the domain of that channel to obtain multiplechannel-specific, threshold images 66. Similarly, a channel-filtermodule 50 may filter the “a” channel of an intermediate image 64corresponding to a “night” condition at multiple threshold values (e.g.,50, 100, 150, 200, 250, or the like or a combination or sub-combinationthereof) within the domain of that channel to obtain multiplechannel-specific, threshold images 66. Using an array of thresholdvalues may account for different intensities of sunlight or other lightsources and different times of the day.

A contour-detection module 52 may delineate or identify the boundarieson one or more objects 68 in one or more channel-specific, thresholdimages 66. In selected embodiments, a contour-detection module 52 mayobtained certain contours from one or more edges. However, the contoursso identified may need to be object contours. Accordingly, the contoursso identified may typically be closed curves.

In the hypothetical, channel-specific, threshold image 66 illustrated inFIG. 5, three objects 68 may be present. The objects 68 correspond to astreet lamp 68 a, red traffic light 68 b, and road sign 68 c.Accordingly, if a contour-detection module 52 were to operate on thischannel-specific, threshold image 66, it may identify three contours,namely, a first contour corresponding to the street lamp 68 a, a secondcontour corresponding to the red traffic light 68 b, and a third contourcorresponding to the road sign 68 c.

A candidate-filter module 54 may use to one or more contours identifiedby a contour-detection module 52 to filter out objects 68 that are notlikely to represent traffic lights 10. In selected embodiments, acandidate-filter module 54 may analyze the size and/or shape of one ormore objects 68 to see if their size and/or shape fits within parametersthat would be expected for a traffic light 10. If the size and/or shapedoes not fit within such parameters, the object 68 may be filter out.

For example, many if not most traffic lights 10 (even those that arearrows) have a width 70 that is substantially equal to their height 72.Accordingly, a candidate-filter module 54 may filter out objects 68 thatdo not have a ratio of width 70 to height 72 that is within a particularrange. For example, in the hypothetical channel-specific threshold image66 illustrated in FIG. 5, three objects 68 are be present. Contoursidentified for the objects 68 may indicate that the street lamp 68 a andthe road sign 68 c have respective widths 70 that are more than twicetheir respective heights 72. Accordingly, a candidate-filter module 54may filter out both objects 68 a, 68 c as having a low likelihood ofrepresenting traffic lights 10.

Alternatively, or in addition thereto, traffic lights 10 will have arelative size in an image 18. That is, they will cover or consume anumber of pixels width-wise, height-wise, and/or area-wise thatcorresponding to a particular range. Accordingly, a candidate-filtermodule 54 may filter out objects 68 that are too large or too small(i.e., that fall outside of the particular range) in width 70, height72, and/or area.

For example, in the hypothetical channel-specific threshold image 66illustrated in FIG. 5, three objects 68 are be present. Contoursidentified for the objects 68 may indicate that the road sign 68 c haswidth 70 that is greater (e.g., consumes more pixels) than would beexpected for a traffic light 10. Accordingly, a candidate-filter module54 may filter out that object 68 c as having a low likelihood ofrepresenting a traffic light 10.

A crop module 56 may crop out one or more regions 74 of interest from animage 18 (e.g., portions 74 of an original RGB image) that correspond toone or more objects 68 that pass 76 through the filtering performed by acandidate-filtering module 54. In selected embodiments, the boundaries78 of the cropping may match or closely track the contours identified bya contour-detection module 52. Alternatively, the boundaries 78 of thecropping may encompass more (e.g., slightly more) than the contoursidentified by a contour-detection module 52 in order to provide a littlemore context.

Once one or more regions 74 of interest have been cropped out, an exportmodule 58 may send them on to an artificial neural network (e.g., anartificial neural network forming part of a neural network module 40)for classification in accordance with the present invention. Thus,classification may be performed only on small portions 74 of an image 18that are likely to contain a traffic light 10. The relatively fastprocessing performed by a computer-vision module 38 may significantlylower the amount of time spent in the relatively slow and/or morecomputationally intense process of classification. Accordingly,detection and classification of traffic lights 10 in accordance with thepresent invention may be a relatively quick process (e.g., be completedin about 1 second or less).

Referring to FIG. 7, in selected embodiments, a process 80 of applyingone or more classification algorithms of an artificial neural network toimage data may begin with obtaining 82 one or more images 18 (e.g., oneor more RGB images 18). The average intensity of a portion 62 (e.g., anupper portion 62) the one or more images 18 may be measured 84. Adetermination may then be made 86 as to whether the average intensity ofthe portion 62 is equal to or greater then a threshold.

If it is, the one or more images 18 may be converted 88 to form one ormore intermediate images 64 in an HSV, HSB, HSL, or HSI color scheme orthe like. Thereafter, the intermediate images 62 may be filtered 90based on their “S” or “saturation” channel at one or more thresholdvalues within the domain of that channel. Conversely, if the averageintensity of the portion 62 is below the threshold, the one or moreimages 18 may be converted 92 to form one or more intermediate images 64in a LAB color scheme or the like. Thereafter, the intermediate images62 may be filtered 94 based on their “a” channel at one or morethreshold values within the domain of that channel.

Filtering 90, 94 in accordance with the present invention may produceone or more channel-specific, threshold images 66. Accordingly, each ofthe one or more channel-specific, threshold images 66 may be processedin order to detect 96 any contours of any candidate objects 68therewithin. Thereafter, candidate objects 68 that do not have a sizeand/or shape within certain ranges like that of a traffic light 10 maybe filtered 98 out. In contrast, regions 74 of interest within the oneor more images 18 (e.g., the original images 18 that were obtained 82)that correspond to (e.g., overlay, occupy the same pixel space as)candidate objects 68 that do have a size and/or shape within certainranges like that of a traffic light 10 may be cropped 100 out andclassified 102 by one or more artificial neural networks.

Thereafter, a vehicle 12 may be controlled 104 based on one or moreclassifications made 102 by the one or more artificial neural networks.Such controlling 104 may include issuing one or more warnings to adriver. Alternatively, or in addition thereto, such controlling 104 mayinclude actively braking to reduce the speed of and/or stop the vehicle12.

The flowchart in FIG. 7 illustrates the architecture, functionality, andoperation of possible implementations of systems, methods, andcomputer-program products according to various embodiments in accordancewith the present invention. In this regard, each block in the flowchartmay represent a module, segment, or portion of code, which comprises oneor more executable instructions for implementing the specified logicalfunction(s). It will also be noted that each block of the flowchartillustration, and combinations of blocks in the flowchart illustration,may be implemented by special purpose hardware-based systems thatperform the specified functions or acts, or combinations of specialpurpose hardware and computer instructions.

It should also be noted that, in some alternative implementations, thefunctions noted in the blocks may occur out of the order noted in theFigure. In certain embodiments, two blocks shown in succession may, infact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. Alternatively, certain steps or functions may beomitted if not needed.

In the above disclosure, reference has been made to the accompanyingdrawings, which form a part hereof, and in which is shown by way ofillustration specific implementations in which the disclosure may bepracticed. It is understood that other implementations may be utilizedand structural changes may be made without departing from the scope ofthe present disclosure. References in the specification to “oneembodiment,” “an embodiment,” “an example embodiment,” “selectedembodiments,” “certain embodiments,” etc., indicate that the embodimentor embodiments described may include a particular feature, structure, orcharacteristic, but every embodiment may not necessarily include theparticular feature, structure, or characteristic. Moreover, such phrasesare not necessarily referring to the same embodiment. Further, when aparticular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to affect such feature, structure,or characteristic in connection with other embodiments whether or notexplicitly described.

Implementations of the systems, devices, and methods disclosed hereinmay comprise or utilize a special purpose or general-purpose computerincluding computer hardware, such as, for example, one or moreprocessors and system memory, as discussed herein. Implementationswithin the scope of the present disclosure may also include physical andother computer-readable media for carrying or storingcomputer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arecomputer storage media (devices). Computer-readable media that carrycomputer-executable instructions are transmission media. Thus, by way ofexample, and not limitation, implementations of the disclosure cancomprise at least two distinctly different kinds of computer-readablemedia: computer storage media (devices) and transmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,solid state drives (“SSDs”) (e.g., based on RAM), Flash memory,phase-change memory (“PCM”), other types of memory, other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store desired program code means inthe form of computer-executable instructions or data structures andwhich can be accessed by a general purpose or special purpose computer.

An implementation of the devices, systems, and methods disclosed hereinmay communicate over a computer network. A “network” is defined as oneor more data links that enable the transport of electronic data betweencomputer systems and/or modules and/or other electronic devices. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a transmission medium. Transmissions media can include anetwork and/or data links, which can be used to carry desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer. Combinations of the above should also be includedwithin the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. The computerexecutable instructions may be, for example, binaries, intermediateformat instructions such as assembly language, or even source code.Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, an in-dash vehicle computer, personalcomputers, desktop computers, laptop computers, message processors,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, mobile telephones, PDAs, tablets, pagers, routers, switches,various storage devices, and the like. The disclosure may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Further, where appropriate, functions described herein can be performedin one or more of: hardware, software, firmware, digital components, oranalog components. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. Certain terms are usedthroughout the description and claims to refer to particular systemcomponents. As one skilled in the art will appreciate, components may bereferred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

It should be noted that the sensor embodiments discussed above maycomprise computer hardware, software, firmware, or any combinationthereof to perform at least a portion of their functions. For example, asensor may include computer code configured to be executed in one ormore processors, and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein purposes of illustration, and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices, as would be known to persons skilled in the relevantart(s).

At least some embodiments of the disclosure have been directed tocomputer program products comprising such logic (e.g., in the form ofsoftware) stored on any computer useable medium. Such software, whenexecuted in one or more data processing devices, causes a device tooperate as described herein.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the disclosure.Thus, the breadth and scope of the present disclosure should not belimited by any of the above-described exemplary embodiments, but shouldbe defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure to the precise form disclosed.Many modifications and variations are possible in light of the aboveteaching. Further, it should be noted that any or all of theaforementioned alternate implementations may be used in any combinationdesired to form additional hybrid implementations of the disclosure.

1. A method for determining which portion or portions of an image toclassify with an artificial neural network, the method comprising:converting an initial frame to an intermediate frame in a differentcolor space or scheme; obtaining at least one threshold frame byfiltering out from the intermediate frame all pixels having below athreshold on a specific channel or component of the different colorspace or scheme; extracting at least one contour from the at least onethreshold frame; cropping out a first portion of the initial frame, thefirst portion encompassing an area including the at least one contour;and classifying, by an artificial neural network, the first portion. 2.The method of claim 1, wherein the initial frame is an RGB frame.
 3. Themethod of claim 2, wherein the different color space or scheme is anon-RGB color space or scheme.
 4. The method of claim 3, wherein theintermediate frame is an HSV, HSB, HSL, or HSI frame.
 5. The method ofclaim 4, wherein the specific channel or component is a saturationchannel or component.
 6. The method of claim 5, wherein: the classifyingcomprises determining an affinity score between the first portion andeach class of a plurality of classes; and the plurality of classesincludes a not-a-traffic-light class, a red-traffic-light class, agreen-traffic-light class, and a yellow-traffic-light class.
 7. Themethod of claim 3, wherein the intermediate frame is a LAB frame.
 8. Themethod of claim 7, wherein the specific channel or component is an “a”channel or component.
 9. The method of claim 8, wherein: the classifyingcomprises determining an affinity score between the first portion andeach class of a plurality of classes; and the plurality of classesincludes a not-a-traffic-light class, a red-traffic-light class, agreen-traffic-light class, and a yellow-traffic-light class.
 10. Themethod of claim 1, wherein the initial frame is an RGB frame and theintermediate frame is a non-RGB frame.
 11. A method comprising:receiving, by a computer system on-board a vehicle, an RGB framecaptured by a forward-looking camera of the vehicle; processing, by thecomputer system, the RGB frame by converting the RGB frame to anintermediate frame in a non-RGB color space or scheme, obtaining atleast one threshold frame by filtering out from the intermediate frameall pixels having below a threshold on a specific channel or componentof the non-RGB color space or scheme, extracting at least one firstcontour from the at least one threshold frame, cropping out a firstportion of the RGB frame, the first portion encompassing a first areaincluding the at least one first contour, and classifying, by anartificial neural network, the first portion.
 12. The method of claim11, wherein the classifying comprises determining an affinity scorebetween the first portion and each class of a plurality of classes thatincludes a not-a-traffic-light class, a red-traffic-light class, agreen-traffic-light class, and a yellow-traffic-light class.
 13. Themethod of claim 12, further comprising controlling braking of thevehicle in accordance with the determining.
 14. The method of claim 11,further comprising comparing, by the computer system before theprocessing, a second portion of the RGB frame to an intensity threshold.15. The method of claim 14, wherein: the intermediate frame in an HSV,HSB, HSL, or HSI frame when the intensity of the second portion of theRGB frame is greater than the intensity threshold; and the intermediateframe in a LAB frame when the intensity of the second portion of the RGBframe is less than the intensity threshold.
 16. The method of claim 15,wherein: the specific channel or component is a saturation channel orcomponent when the intensity of the second portion of the RGB frame isgreater than the intensity threshold; and the specific channel orcomponent is an “a” channel or component when the intensity of thesecond portion of the RGB frame is less than the intensity threshold.17. The method of claim 16, wherein the classifying comprisesdetermining an affinity score between the first portion and each classof a plurality of classes that includes a not-a-traffic-light class, ared-traffic-light class, a green-traffic-light class, and ayellow-traffic-light class.
 18. A system comprising: a vehicle; at leastone forward-looking camera secured to the vehicle and configured tocapture an initial frame; at least one processor carried on-board thevehicle; and memory operably connected to the at least one processor,the memory storing a computer-vision module programmed to process theinitial frame by (1) converting the initial frame to an intermediateframe in a different color space or scheme, (2) obtaining at least onethreshold frame by filtering out from the intermediate frame all pixelshaving below a threshold on a specific channel or component of thedifferent color space or scheme, (3) extracting at least one firstcontour from the at least one threshold frame, and (4) cropping out afirst portion of the initial frame, the first portion encompassing afirst area including the at least one first contour, and a neuralnetwork module programmed to determine an affinity score between thefirst portion and each class of a plurality of classes.
 19. The systemof claim 18, wherein the plurality of classes includes anot-a-traffic-light class, a red-traffic-light class, agreen-traffic-light class, and a yellow-traffic-light class.
 20. Thesystem of claim 19, wherein the memory further comprises a controlmodule programmed to control braking of the vehicle in accordance withdeterminations made by the neural network module.